期刊
ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
卷 249, 期 1, 页码 -出版社
IOP Publishing Ltd
DOI: 10.3847/1538-4365/ab917f
关键词
Galaxies; Neural networks; Galaxy photometry
资金
- research project grant Fundamental Physics from Cosmological Surveys - Swedish Research Council (VR) [Dnr 2017-04212]
- National Science Foundation [PHY-1607611]
- NSF Astronomy and Astrophysics Postdoctoral Fellowship [AST-1701487]
- Simons Foundation
- UCL Cosmoparticle Initiative
We presentspeculator-a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating spectra, we use a principal component analysis to construct a set of basis functions and neural networks to learn the basis coefficients as a function of the SPS model parameters. For photometry, we parameterize the magnitudes (for the filters of interest) as a function of SPS parameters by a neural network. The resulting emulators are able to predict spectra and photometry under both simple and complicated SPS model parameterizations to percent-level accuracy, giving a factor of 10(3)-10(4)speedup over direct SPS computation. They have readily computable derivatives, making them amenable to gradient-based inference and optimization methods. The emulators are also straightforward to call from a GPU, giving an additional order of magnitude speedup. Rapid SPS computations delivered by emulation offers a massive reduction in the computational resources required to infer the physical properties of galaxies from observed spectra or photometry and simulate galaxy populations under SPS models, while maintaining the accuracy required for a range of applications.
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